Local Directional Number Pattern for Face
Analysis: Face and Expression Recognition
ABSTRACT
This paper proposes a novel local feature descriptor, local directional number pattern
(LDN), for face analysis, i.e., face and expression recognition. LDN encodes the directional
information of the face’s textures (i.e., the texture’s structure) in a compact way, producing a
more discriminative code than current methods. We compute the structure of each micropattern with the aid of a compass mask that extracts directional information, and we encode
such information using the prominent direction indices (directional numbers) and sign—
which allows us to distinguish among similar structural patterns that have different intensity
transitions. We divide the face into several regions, and extract the distribution of the LDN
features from them. Then, we concatenate these features into a feature vector, and we use it
as a face descriptor. We perform several experiments in which our descriptor performs
consistently under illumination, noise, expression, and time lapse variations. Moreover, we
test our descriptor with different masks to analyze its performance in different face analysis
tasks.
ARCHITECTURE
Existing System
In this Existing System, This recognition problem is made difficult by the great
variability in head rotation and tilt, lighting intensity and angle, facial expression, aging, etc.
Some other attempts at facial recognition by machine have allowed for little or no variability
in these quantities. Yet the method of correlation (or pattern matching) of unprocessed optical
data, which is often used by some researchers, is certain to fail in cases where the variability
is great. In particular, the correlation is very low between two pictures of the same person
with two different head rotations.
Disadvantage
Where face recognition does not work well include poor lighting, sunglasses, long
hair, or other objects partially covering the subject’s face, and low resolution images. Another
serious disadvantage is that many systems are less effective if facial expressions vary. Even a
big smile can render the system less effective.
Proposed System
In this Proposed System, we propose a face descriptor, Local Directional Number
Pattern (LDN), for robust face recognition that encodes the structural information and the
intensity variations of the face’s texture. LDN encodes the structure of a local neighbourhood
by analyzing its directional information. Consequently, we compute the edge responses in the
neighbourhood, in eight different directions with a compass mask. Then, from all the
directions, we choose the top positive and negative directions to produce a meaningful
descriptor for different textures with similar structural patterns. This approach allows us to
distinguish intensity changes.
Advantage
1. Robust against illumination changes
2. Performance Better
3. Compact Mode
ALGORITHM - PRINCIPAL COMPONENT ANALYSIS
PCA finds a linear projection of high dimensional data into a lower dimensional
subspace such as:

The variance retained is maximized.

The least square reconstruction error is minimized.

LSI: Latent Semantic Indexing.

Kleinberg/Hits algorithm (compute hubs and authority scores for nodes).

Google/Page Rank algorithm (random walk with restart).

Image compression (Eigen faces)

Data visualization (by projecting the data on 2D).
Modules
1. Local Direction Number Pattern (LDN)
2. Face Expression
a. Eigen Faces
b. Fisher Faces
3. Results between Eigen and Fisher Faces
Modules Description
1. Local Direction Number Pattern
In this module, LDN is a six bit binary code assigned to each pixel of an input image
that represents the structure of the texture and its intensity transitions. we create our pattern
by computing the edge response of the neighbourhood using a compass mask, and by taking
the top directional numbers, that is, the most positive and negative directions of those edge
responses.
2. Face Expression
In this Module, each face is represented by a LDN histogram (LH). The LH contains fine
to coarse information of an image, such as edges, spots, corners and other local texture
features. Given that the histogram only encodes the occurrence of certain micro-patterns
without location information, to aggregate the location information to the descriptor.
a. Eigen Faces
In this module, facial recognition is discriminating input signals (image data) into
several classes (persons). The input signals are highly noisy (e.g. the noise is caused by
differing lighting conditions, pose etc.), yet the input images are not completely random and
in spite of their differences there are patterns which occur in any input signal. Such patterns,
which can be observed in all signals could be - in the domain of facial recognition - the
presence of some objects (eyes, nose, mouth) in any face as well as relative distances
between these objects. These characteristic features are called eigenfaces in the facial
recognition domain (or principal components generally). They can be extracted out of
original image data by means of a mathematical tool called Principal Component Analysis
(PCA). By means of PCA one can transform each original image of the training set into a
corresponding eigenfaces. An important feature of PCA is that one can reconstruct any
original image from the training set by combining the eigenfaces. Remember that eigenfaces
are nothing less than characteristic features of the faces. Therefore one could say that the
original face image can be reconstructed from eigenfaces if one adds up all the eigenfaces
(features) in the right proportion.
b. Fisher Faces
In this Module, bit harder to explain, because they identify regions of a face that
separate faces best from each other. None of them seems to encode particular light settings; at
least it's not as obvious as in the Eigenfaces method. If I could only guess which component
describes which features? So we leave the interpretation up to the reader. What we lose with
the Fisher faces method for sure, is the ability to reconstruct faces. If I want to reconstruct
faces, just like in the Eigenfaces section.
3. Results between Eigen and Fisher Faces
Fisher faces
Eigen Faces
Computational
Slightly
complexity
complex
Effectiveness
Good, even with Some with enough
across pose
limited data
Sensitivity
lighting
to Little
more Simple
data
very
HARDWARE REQUIREMENTS

System
:
Pentium IV 2.4 GHz.

Hard Disk
:
80 GB.

Monitor
:
15 VGA Colour.

Mouse
:
Logitech.

Ram
:
512 MB.
SOFTWARE REQUIREMENTS

Operating system
:
Windows 8 (32-Bit)

Front End
:
Visual Studio 2010

Coding Language
:
C#.NET